Radiological imaging is an essential component of a swallowing assessment. Artificial intelligence (AI), and especially deep learning (DL) models, have enhanced the efficiency and efficacy through which imaging is interpreted, and subsequently have important implications for swallow diagnostics and intervention planning. However, the application of AI for the interpretation of videofluoroscopic swallowing studies (VFSS) is still emerging. This review showcases recent literature in the use of AI to interpret VFSS and highlights clinical implications for speech pathologists (SPs). With a surge in AI research, there have been advances made in dysphagia assessment. Several studies have demonstrated successful implementation of DL algorithms to analyze VFSS. Notably, convolutional neural networks (CNNs) have been used to detect pertinent aspects of the swallowing process with high levels of precision. DL algorithms have the potential to streamline VFSS interpretation, improve efficiency and accuracy, and enable precise interpretation of instrumental dysphagia evaluation, which is especially advantageous when access to skilled clinicians is not ubiquitous. By enhancing precision, speed, and depth of VFSS interpretation, SPs can obtain a more comprehensive understanding of swallow physiology and deliver targeted and timely intervention that is tailored towards the individual. This has practical applications for both clinical practice and dysphagia research. As this research area grows and AI technologies progress, the application of DL in the field of VFSS interpretation is clinically beneficial and has the potential to transform dysphagia assessment and management. With broader validation and inter-disciplinary collaborations, AI-augmented VFSS interpretation is likely to transform swallow evaluation and ultimately improve outcomes for individuals with dysphagia.

In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human-machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels, some have low, and others high levels. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast the individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few effects that can be generalized to human-machine interactions; by the replication crisis of today (e.g., the invalid scales for self-esteem, implicit racism, and honesty); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us to theorize about the bistability of mind and body, but it proposes a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We begin with a review of the science of teams and a focus on human-machine team research in the laboratory versus the open; justifications for rejecting traditional social science while supporting our approach; a full understand of the complexity of the domain, tasks in the domain, and how teams address both; the mathematics involved; a review of results from our model versus the open field; a discussion of the results; conclusions; and the path forward to successfully advance the science of interdependence and autonomy.

Even if the field of medicine has made great strides in recent years, infectious diseases caused by novel viruses that damage the respiratory system continue to plague people all over the world. This type of virus is very dangerous, especially for people who deal with serious long-term breathing problems, like triggering asthma, pneumonia, or bronchitis infections. Thus, this paper demonstrates a new Secure Machine Learning Monitoring System for a model for virus detection. Our proposed model makes use of 4 basic emerging technologies, Internet of Things (IoT), Wireless Sensor Networks (WSN), Cloud Computing (CC), and Machine Learning (ML), to detect dangerous types of viruses that infect people or animals causing panic worldwide and deregulating human daily life. The proposed system is a robust system that could be established in various buildings, like hospitals, entertainment halls, universities, etc., and will provide accuracy, speed, and privacy for data collected in the detection of viruses.

According to a recent study by OpenAI, Open Research, and the University of Pennsylvania, large language models (LLMs) based on artificial intelligence (AI), such as Generative Pretrained Transformers (GPTs), may have potential implications on the job market, specifically regarding job occupations that demand writing or programming skills. The research points out Interpreters and Translators as one of the main job occupations with greater exposure to AI in the US job market (76.5%). This article, following a mixed-methods survey-based research, provides insights into the Portuguese Language Service Providers (LSPs) awareness and knowledge about AI, specifically regarding neural machine translation (NMT) and large language models (LLM), its actual use and usefulness, as well as their potential influence on work performance and labour market. The results show that most professionals are unable to identify if AI and/or automation technologies support the tools that are most used in the profession. The usefulness of AI is essentially low to moderate and the professionals who are less familiar with it and less knowledgeable also demonstrate lack of trust in it. Two thirds of the sample estimate negative or very negative effects of AI in the profession, expressing as main concerns the devaluation and replacement of experts, the reduction of income, and the reconfiguration of the career of translator to mere post-editors.

Lung cancer is the leading cause of cancer death worldwide, with non-small cell lung cancer (NSCLC) making up 80% of cases. Some genetic factors leading to NSCLC development include genetic mutations and PD-L1 expression. PD-L1 proteins are targeted in an NSCLC treatment called targeted gene therapy. However, this treatment is effective in a low percentage of patients. This study aimed to create machine learning models to use features like the number of mutations and the level of PD-L1 proteins in cancer cells, along with others, to predict whether a patient will receive clinical benefit from gene therapy treatment. This was done by downloading and merging datasets from cbioportal.org to create a sample size for the model. Features with high correlations to clinical benefit were identified. Three machine-learning models were created using these features to predict clinical benefits in patients, and each model’s accuracy was evaluated. All three models were accurate between 55-85%, with two of the models averaging an accuracy around 75%. Doctors can use these models to more accurately predict whether gene therapy treatment is likely to work in a patient before prescribing it to them.

Despite being an abundant marine organism in Indonesia, black sea cucumber is still underutilised due to its slightly bitter taste. Previous studies have hinted at the potential of black sea cucumber as an anti-cancer agent. However, specific identification of bioactive compounds that can interact with cancer proteins is still lacking. In the same place, cancer ranks third as Indonesia's leading cause of death. Therefore, this study aims to identify potential anti-cancer compounds from black sea cucumbers using a comprehensive in silico drug discovery approach. This research uses machine learning, molecular docking, and ADMET analysis to identify bioactive compounds that specifically interact with cancer proteins. A combination of the Cascade Deep Forest algorithm and ECFP-AAIndex1 feature combination proved to be the most effective in predicting these interactions. Through molecular docking validation, four bioactive compounds with strong binding affinity were identified: Afimoxifene, Danazol, Taxifolin, and Terfenadine. ADMET analysis highlighted Taxifolin as the most promising candidate, as it passed most ADMET parameters. Further wet laboratory studies are required to confirm the effects and potential of these compounds as anti-cancer agents. This study builds a foundation for future investigations into alternative cancer treatments using abundant natural resources.

In this work, an IoT system with edge computing capability is proposed facilitating postoperative surveillance of patients who have undergone knee surgery. The main objective is to reliably identify whether a set of orthopedic rehabilitation exercises are executed correctly, which is critical since, it is often necessary to supervise patients during the rehabilitation period so as to avoid injuries or long recovery periods. The proposed system leverages Internet of Things (IoT) paradigm, in combination with Deep Learning and Edge Computing to classify the extend-flex movement of one’s knee via embedded Machine Learning (ML) classification algorithms. The contribution of the proposed work is multilayered. Furthermore as an outcome of this work a dataset of labeled knee movements is freely available on https://www.kaggle.com/datasets/billskarm/knee-range-of-motion to the research community. It also provides real time movement detection with an accuracy reaching 100%, which is achieved with a ML model trained to fit a low cost, off the shelf, Bluetooth Low Energy platform. The proposed Edge Computing approach allows predictions to be performed on-device rather than solely relying on a cloud service. This yields critical benefits in terms of wireless bandwidth and power conservation, drastically enhancing device autonomy, while delivering reduced event detection latency. In particular, the “on device” implementation is able to yield a drastic 99,9% wireless data transfer reduction, a critical 39% prediction delay reduction and a valuable 17% increase of prediction event. Finally, enhanced privacy comprises another significant benefit from the implemented Edge Computing ML model as sensitive data can be processed on-site and only events or predictions are shared with the medical personnel.

The dominance of social media has added to the channels of bullying to perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in today’s cyber world and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance issue and generalisation. In recent years, large language models (LLM) like BERT and RoBERTa have achieved state of the art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed RoBERTa outperformed other models.

A high performance Driver State Monitoring (DSM) application for the detection of driver drowsiness is presented in this paper. It relies on the usage of an Ensemble of Regression Trees (ERTs) machine learning method that aligns 68 facial landmarks. Special focus is given on the acceleration of the frame processing using reconfigurable hardware. Reducing the frame processing latency saves time that can be used to apply frame-to-frame facial shape coherency rules. False face detection and false shape estimations can be ignored for higher robustness and accuracy in the operation of the DSM application without reducing the frame processing rate that can reach 65 frames per second. The sensitivity and precision in yawning recognition can reach 93% and 97%, respectively. The implementation of the employed DSM algorithm in reconfigurable hardware is challenging since the kernel arguments require large data transfers and the degree of data reuse in the computational kernel is low. Due to this, unconventional hardware acceleration techniques have been employed that can also be useful for the acceleration of several other applications.

This paper presents a cascade predictive control structure based on the field-oriented control (FOC) in the dq rotor reference frame for the synchronous reluctance machine (SynRM). The constant d-axis current control strategy was used and thus, the electromagnetic torque was directly controlled by the q-axis current. Because the model of the two axes currents from the inner loop is a coupled non-linear multivariable one, to non-interaction linear control the two currents, their decoupling was achieved through feedforward components. Following the decoupling, two independent monovariable linear systems resulted for the two currents dynamics that were controlled using model predictive control (MPC) algorithms, considering their ability to automatically handle the state bounds. The most important bounds for SynRM are the limits imposed on currents and voltages, which in the dq plane correspond to a circular limit. To avoid computational effort, linear limitations were adopted through polygonal approximations, resulting in rectangular regions in the dq plane. For the outer loop that controls the angular speed with a constrained MPC algorithm, as plant was considered the q-axis current closed loop dynamics and the torque linear equation. To evaluate the performances of the proposed cascade predictive control structure, a simulation study using MPC controllers versus PI ones was conducted.

Background and Purpose: Open-source Artificial intelligence (AI) applications are fast transforming access to information and allow students to prepare assignments and offer quite accurate responses to a wide range of exam questions which are routinely used in assessments of students across the board including undergraduate dental students. This study aims to evaluate the performance ChatGPT, an AI-based application, on a wide range of dental assessments and discusses the implications for undergraduate dental education. Methods: This was an exploratory study investigating the accuracy of ChatGPT to attempt a range of recognized assessments in undergraduate dental curricula. ChatGPT was used to attempt ten items based on each of the five commonly used question formats including single-best answer (SBA) multiple-choice questions (MCQs); short answer questions (SAQs); short essay questions (SEQs); True/False questions and fill in the blanks items. In addition, ChatGPT was used to generate reflective reports based on multisource feedback (MSF); research methodology; critical appraisal of the literature. Results: ChatGPT application provided accurate responses to majority of knowledge-based assessments based on MCQs, SAQs, SEQs, Tue/False and fill in the blanks items. However, it was only able to answer text-based questions and did not allow processing of questions based on images. Responses generated to written assignments were also of good quality apart from those for critical appraisal of literature. Word count was the key limitation observed in outputs by ChatGPT as it was only able to produce reports limited to approximately 650 words. Conclusion: Notwithstanding their current limitations, AI-based applications have the potential to revolutionize virtual learning. Instead of treating it as a threat, dental educators need to adapt teaching and assessments in dental education to the benefits of the learners whilst mitigating against dishonest use of AI-based applications.

Aim: This study aimed to evaluate the sociodemographic, behavioral, and biological profile and its relationship with the emergence of chronic non-communicable diseases in riverside populations in the Xingu region, Pará, Brazil. Methods: Characteristics related to health indicators and which risk factors are considered most important were also analyzed. This is a cross-sectional, exploratory, and descriptive study. The sample consisted of riverside people over 18 years of both sexes. The sample size (n.86) was calculated with a confidence level of 95% and a sample error of 5%. To divide the groups, the K-means clustering algorithm was adopted through an unsupervised method and the values were expressed as median. For continuous and categorical data, the Mann-Whitney and Chi-square tests were used, respectively, and the significance level was set at p<5%. To classify the degree of importance of each variable, the multi-layer perceptron algorithm was applied. Results: Based on this information, the sample was divided into two groups: the group with low or no education, with bad habits and worse health conditions, and the group with opposite characteristics. The risk factors considered for cardiovascular diseases and diabetes in the groups were low education (p < 0.001), sedentary lifestyle (p < 0.01), smoking, alcoholism, body mass index (p < 0.05) and the waist-hip ratio, with values above the expected, in both groups. Conclusions: The factors considered important to have a good health condition or not were the educational and social conditions of these communities and a part of the riverside population was considered healthier than the other.

In this work, we adapt the fine-tuning algorithm of the Naïve Bayesian (FTNB) classifier to make it more suitable for imbalanced datasets. In particular, we boost misclassified instance probability terms by an amount that is disproportional to the harmonic mean of actual and predicted classes. The intuition is that discriminative attributes when the instance is misclassified would have small probability term pair values in both the actual class due to data scarcity and the predicted class due to weak correlation. Conversely, if both values are relatively high, then the attribute has good data coverage (support) and it should not be a cause for misclassification. Since the harmonic average is dominated by the smaller value and we have an imbalanced dataset, we should enact a large update if both or either term probabilities of actual and predicted classes are small. We used several benchmark datasets (60 different balanced and imbalanced datasets) to determine if the poor performance of the NB classifier is due to the scarcity of data and compared the performance of the proposed algorithm with NB, original FTNB, and other relatively new SOTA Ensemble Imbalanced Classifiers. Our empirical results reveal that the new proposed algorithm significantly outperforms all other classifiers.

The national-scale evaluation and modelling of the impact of agricultural management and cli-mate change on soils, crop growth, and the environment require soil information at a spatial res-olution addressing individual agricultural fields. This manuscript presents a data science ap-proach which agglomerates the soil parameter space into a limited number of functional soil pro-cess units (SPUs) which may be used to run agricultural process models. In fact, two unsupervised classification methods were developed to generate a multivariate 3D data product consisting of SPUs, each being defined by a multivariate parameter distribution along the depth profile from 0 to 100 cm. The two methods account for differences in variable types and distributions and in-volve genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both, their soil characteristics and landscape setting. The high potential of the methods was demonstrated by applying them to the agricultural German soil landscape. The resulting data product consists of twenty SPUs. It has a 100 m raster resolution in the 2D mapping space, and its resolution along the depth profile is 1 cm. It includes the soil properties texture, stone content, bulk density, hydromorphic properties, total organic carbon content, and pH.

This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.

Many dedicated designs for real-time operations provide functionality on fixed-sized operators, but where speed, scalability, and flexibility are required, extensive research is demanded. Dedicated designs can provide real-time processing for many applications. This paper presents an FPGA-based design of a general image processor. The proposed design is based on a fixed-point representation of binary numbers. The proposed design provides a mechanism to manage matrices on-chip along with matrix arithmetic. The matrices are represented with simple identifiers and microinstruction that assist in the computation of many operations which are useful for solving complex problems. The design was successfully implemented and tested using VHDL language. The proposed design is an efficient architecture as a standalone processor with all embedding computational resources necessary for an embedded image processing application.

This paper provides a methodology for the production of synthetic images for training neural networks to recognise shapes and objects. There are many scenarios in which it is difficult, expensive and even dangerous to produce a set of images that is satisfactory for the training of a neural network. The development of 3D modelling software has nowadays reached such a level of realism and ease of use that it seemed natural to explore this innovative path and to give an answer regarding the reliability of this method that bases the training of the neural network on synthetic images. The results obtained in the two proposed use cases, that of the recognition of a pictorial style and that of the recognition of migrants at sea, leads us to support the validity of the approach, provided that the work is conducted in a very scrupulous and rigorous manner, exploiting the full potential of the modelling software. The code produced, which automatically generates the transformations necessary for the data augmentation of each image, and the generation of random environmental conditions in the case of Blender and Unity3D software, is available under the GPL licence on GitHub. The results obtained lead us to affirm that through the good practices presented in the article, we have defined a simple, reliable, economic and safe method to feed the training phase of a neural network dedicated to the recognition of objects and features, to be applied to various contexts.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

Cytosine (C) to uracil (U) RNA editing is one of the most important post-transcriptional processes, however exploring C-to-U editing events efficiently within the crop mitochondrial genome remains a challenge. An improving predictive RNA editor for crop mitochondrial genomes, iPReditor-CMG, was proposed, which was based on SVM, three common crop mitochondrial genomes and self-sequenced tobacco mitochondrial ATPase. After multi-combination feature extracting, high-dimension feature screening and multi-test independent predicting, the results showed that the average accuracy of intraspecific prediction was 0.85, and the highest value even up to 0.91, which outperformed the previous reference models. While the prediction accuracies were 0.78 between dicotyledons and no more than 0.56 between dicotyledons and monocotyledons, implying a possible similarity in C-to-U editing mechanisms among close relatives. The best model was finally identified with an independent test accuracy of 0.91 and an area under the curve of 0.88, and further suggested that five unreported feature sequences TGACA, ACAAC, GTAGA, CCGTT and TAACA were closely associated with the editing phenomenon. Multiple evaluation findings supported that the iPReditor-CMG could be effectively applied to predict crop mitochondrial editing sites, which may contribute to insight into their recognition mechanisms and even other post-transcriptional events in crop mitochondria.

The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.

Egyptian cotton is one of the most important commodities to the Egyptian economy and is renowned globally for its quality, which is currently graded by manual inspection. This has several drawbacks including significant labour requirement, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This current work uses a low-cost colour vision system, combined with machine learning to predict the cotton lint grade of the cultivars Giza 86, 97, 90, 94 and 96. Unsupervised and supervised machine learning approaches were explored and compared. Three different supervised learning algorithms were evaluated: linear discriminant analysis, decision trees and ensemble modelling. The highest accuracy models (77.3-98.2%) used an ensemble modelling technique to classify samples within the Egyptian cotton grades: Fully Good, Good, Fully Good Fair, Good Fair and Fully Fair. The unsupervised learning technique k-means showed that human error is more likely to occur when classifying lint belonging to the higher quality grades and underlined the need for an intelligent system to replace manual inspection.

The present study aims to develop a risk-based approach to find optimal solutions for life extension management for offshore wind farms based on Markowitz’s modern portfolio theory, adapted from finance. The developed risk-based approach assumes that the offshore wind turbines (OWT) can be considered as cash-producing tangible assets providing positive return from the initial investment (capital) with a given risk attaining the targeted (expected) return. In this regard, the present study performs a techno-economic life extension analysis within the scope of the multi-objective optimisation problem. The first objective is to maximise the return from the overall wind assets, while the latter aims to minimise the risk associated with obtaining the return. In formulating the multi-dimensional optimisation problem, the life-extension assessment considers the results of a detailed structural integrity analysis, free-cash-flow analysis, and probability of project failure, local and global economic constraints. Further, the risk is identified as the variance from the expected mean of return on investment. The risk-return diagram is utilised to classify the OWTs of different classes using an unsupervised machine learning algorithm. The optimal portfolios for the various required rate of return are recommended for different stages of life extension.

We propose a chemical language processing model to predict polymers’ glass transition temperature (Tg) through a polymer language (SMILES, Simplified Molecular Input Line Entry System) embedding and recurrent neural network. This model only receives the SMILES strings of polymer’s repeat units as inputs and considers the SMILES strings as sequential data at the character level. Using this method, there is no need to calculate any additional molecular descriptors or fingerprints of polymers, and thereby, being very computationally efficient and simple. More importantly, it avoids the difficulties to generate molecular descriptors for repeat units containing polymerization point `*’. Results show that the trained model demonstrates reasonable prediction accuracy on unseen polymer’s Tg. Besides, this model is further applied for high-throughput screening on an unlabeled polymer database to identify high-temperature polymers that are desired for applications in extreme environments. Our work demonstrates that the SMILES strings of polymer’s repeat units can be used as an effective feature representation to develop a chemical language processing model for predictions of Tg. The framework of this model is general and can be used to construct structure-property relationships for other polymer’s properties.

Artificial Intelligence and especially machine learning have noticed rapid advancement on image processing operations. However, its involvement in the architectural design is still in its initial stages compared to other disciplines. Therefore, this paper addresses the issues of developing an integrated bottom up digital design approach and details a research framework for the incorporation of Deep Convolutional Generative Adversarial Network (GAN) for early stage design exploration and generation of intricate and complex alternative facade designs for urban infill. This paper proposes a novel building facade design by merging two neighboring building’s architecture style, size, scale, openings, as reference to create a new building design in the same neighborhood for urban infill. This newly produced building contains the outline, style and shape of the parent buildings. A 2D urban infill building design is generated as a picture where 1) neighboring buildings are imported as a reference using mobile phone and 2)iFACADE decode their spatial adjacency. It is depicted the iFACADE will be useful for designers in the early design stage to generate new façades depending on existing buildings in a short time that will save time and energy. Besides, building owners can use iFACADE to show their architects their preferred architecture facade by mixing two building styles and generating a new building. Therefore, it is depicted that iFACADE can become a communication platform in the early design stages between architects and owners. Initial results properly define a heuristic function for generating abstract design facade elements and sufficiently illustrate the desired functionality of our developed prototype.

The relationship between the street environment and the health, education, mobility, and criminal behaviors of its citizens has long been investigated by economists, sociologists and urban planners. Home buyers were found to pay a premium for better street appearance. Prior studies considering streetscapes mainly focus on objective measures such as the number of nearby trees, the tree canopy area, or the view index of physical features such as greenery, sky or building. However, subjective perceptions may have complex or subtle relationships to physical features, individual physical features or simply summing them up do not capture people’s comprehensive perception. In contrast, this study proposed a new approach for the urban-scale application to quantify both subjectively and objectively measured streetscape scores for six important perception qualities, namely Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity. Built on prior quantitative studies in urban design quality and emerging applications in deep learning and open source street view imagery for urban perceptions, we integrated existing frameworks to (1) effectively collect and evaluate both subjectively and objectively- measured perceptions; (2) investigate the coherence and divergence in ML-predicted subjective scores and formula-derived objective scores; and (3) compare their effects in affecting house prices taking Shanghai as a case study using a large-scale dataset on home transactions. The results implied: first, the percentage increase in sales price attributable to street scores is significant for both subjective and objective measurements. In general subjective scores explained more variance over structural attributes and objective scores in hedonic price model. Particularly, objective Greenness, subjective Safety and Imageability scores positively affected house prices. Second, for Greenness and Imageability scores, the subjective and objective measures exhibited opposite signs in affecting house prices, which implied that there might be mechanisms related to the psychological, social-demographical characteristics of street users that have not been fully incorporated by objective measures that taking view indices or recombination of them. In addition, certain objective measure might outperform subjective counterpart when the connotation of the perception is self-evident and not complicated, for example the Greenness. For those concepts were not familiar to the average person, subjective framework exhibits better performance. This is the first study comprehensively expanding hedonic price method with both subjectively and objectively measured streetscape qualities. It suggested that city authorities could levy a street environment tax to compensate the public budget invested in street environment where developers secured benefits from a price premium. This study enriches our understanding of the economic values of the subjective and objective measures street qualities. It sheds light on promising future study areas where the coherence and divergence of the two measurements should be further stressed.

In recent years, since the propagation channel characteristics have been effectively used for applications such as motion sensing, position detection, etc. A great deal of attention is attracted to channel sounding methods easy to utilize using low-cost devices. This paper presents a device-free indoor location estimation method using spatio-temporal features of radio propagation channels using the 2.4-GHz band 3-by-3 MIMO channel sounder developed using commodity wireless LANs. The measurement results demonstrated a reasonable performance of the proposed method with small number of antennas.

Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine-learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, each decoder specializes in decoding words from a specific region of the channel words' distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high SNRs.

In Lagrangian dynamics, the detection of coherent clusters can help understand the organization of transport by identifying regions with coherent trajectory patterns. Many clustering algorithms, however, rely on user-input parameters, requiring a priori knowledge about the flow and making the outcome subjective. Building on the conventional spectral clustering method of Hadjighasem et al (2016), a new parameter-free spectral clustering approach is developed that automatically identifies parameters and does not require any user-input choices. A noise-based metric for quantifying the coherence of the resulting coherent clusters is also introduced. The parameter-free spectral clustering is applied to two benchmark analytical flows, the Bickley Jet and the asymmetric Duffing oscillator, and to a realistic, numerically-generated oceanic coastal flow. In the latter case, the identified model-based clusters are tested using observed trajectories of real drifters. In all examples, our approach succeeded in performing the partition of the domain into coherent clusters with minimal inter-cluster similarity and maximum intra-cluster similarity. For the coastal flow, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different.

ClinVar is a web platform that stores around 774k curated entries, which allows exploring genetic variants and their associations with complex phenotypes. A partial set of ClinVar’s genetic associations were reported with conflict of interpretation or uncertain clinical impact significance, which currently challenges clinicians and geneticists. Here, we evaluate the performance of data pre-processing methods combined with classical prediction methods, such as Naive Bayes, Random Forest, and Support Vector Machine to build a meta-prediction model aiming to improve genetic pathogenicity interpretation. Models were trained with ClinVar data (September 2020), and genetic variants were annotated with eight functional impact predictors catalogued with SnpEff/SnpSift (v4.3). A 10-fold cross-validation strategy was performed for evaluation by accuracy, F1-Score, Receiver Operating Characteristic, Area Under Curve. The best meta-prediction model raises by combining one-hot encoding with tree-based classifiers as Random Forest, which shows Area Under Curve ≥ 0,93. We predict pathogenicity for 109k genetic variants, which were found labeled as uncertain significance or conflict of interpretation. Additionally, we implemented AmazonForest (https://www.lghm.ufpa.br/amazonforest), a web tool to query data for a set of 5k variants that were predicted with high pathogenic probability (RFprob >= 0.9).

This paper presents a multi-year research study conducted at the University of Toledo, aimed at improving human-machine teaming (HMT) methods and technologies. With the incorporation of artificial intelligence (AI) into 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened a path for the new area of HMT. When it comes to crucial tasks such as patient treatment and care, defense, and industrial production, the use of non-standardized HMT technologies may pose a risk to human lives as well as up to billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we aim to address the lack of HMT design and development standards. We propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and human team. Followed by processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method using two controlled HMT scenarios; first, emergency care provider (ECP) training, and second, patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. As part of these HMT simulation studies, the impact of each parameter related to machines, humans, and HMT (such as errors made by humans and by the VBSA) is presented from the perspective of performance, rules, roles, and operation limitations. In addition, based on the analysis of results from two case studies, we presented guidelines for designing future HMT. Our conclusions indicate that the proposed HIL can be used to perform standardization studies in the pursuit of developing techniques for benchmarking HMT that can be used in critical situations.

In order to design efficient and sustainable office spaces and to automate lighting, heating and air circulation in these facilities, solving the challenge of occupancy prediction is crucial. In office spaces where large areas need to be observed, multiple sensors must be used for full coverage. In these cases, it is normally important to keep the costs low, but also to make sure that the privacy of the people who use such environments are preserved. Low-cost and low-resolution heat (thermal) sensors can be very useful to build solutions that address these concerns. However, they are extremely sensitive to noise artifacts which might be caused by heat prints of the people who left the space or by other objects which are either using electricity or exposed to sunlight. There are some earlier solutions for occupancy prediction that employ low-resolution heat sensors, however, they have not addressed nor compensate for such heat artifacts. Therefore, in this paper, we present a low-cost and low-energy consuming smart space implementation to predict the number of people in the environment based on whether their activity is static or dynamic in time. We use a low-resolution (8×8) and non-intrusive heat sensor to collect data from an actual meeting room. We propose two novel workflows to predict the occupancy; one based on computer vision and one based on machine learning. Besides comparing the advantages and disadvantages of these different workflows, we use several state-of-the-art explainability methods in order to provide a detailed analysis of the algorithm parameters and how the image properties influence the resulting performance. Furthermore, we analyze noise resources which affect the heat sensor data. We hope that our analysis brings light into understanding how to handle very low-resolution heat images in these environments. The presented workflows could be used in various domains and applications other than smart offices, where occupancy prediction is essential, e.g., for elderly care.

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter $\mathsf{\gamma }$—are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.

We present a general process for the halting problem, valid regardless of the time and space computational complexity of the decision problem. It can be interpreted as the maximization of entropy for the utility function of a given Shannon-Kolmogorov-Bernoulli process. Applications to non-polynomials problems are given. The new interpretation of information rate proposed in this work is a method that models the solution space boundaries of any decision problem (and non polynomial problems in general) as a communication channel by means of Information Theory. We described a sort method that order objects using the intrinsic information content distribution for the elements of a constrained solution space - modeled as messages transmitted through any communication systems. The limits of the search space are defined by the Kolmogorov-Chaitin complexity of the sequences encoded as Shannon-Bernoulli strings. We conclude with a discussion about the implications for general decision problems in Turing machines.

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R² equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.

The upsurge of Generative Adversarial Networks (GANs) in the previous five years has led to advancements in unsupervised data manipulation, sourced feature translation, and precise input-output synthesis through a competitive optimization of the discriminator and generator networks. More specifically, the recent rise of cycle-consistent GANs enables style transfers from a discrete source (input A) to target domain (input B) by preprocessing object features for a multi-discriminative adversarial network. Traditionally, cyclical adversarial networks have been exploited for unpaired image-to-image translation and domain adaptation by determining mapped relationships between an input A graphic and an input B graphic. However, this integral mechanism of domain adaptation can be applied to the complex acoustical features of human speech. Although well-established datasets, such as the 2018 Voice Conversion Challenge repository, paved way for female-male voice transformation, cycle-GANs have rarely been re-engineered for voices outside the datasets. More critically, cycle-GANs have massive potential to extract surface-level and hidden feature to distort an input A source into a texturally unrelated target voice. By preprocessing, compressing, and packaging unique acoustical voice properties, CycleGANs can learn to decompose speech signals and implement new translation models while preserving emotion, the intent of words, rhythm, and accents. Due to the potential of CycleGAN’s autoencoder in realistic unsupervised voice-voice conversion/feature adaptation, the researchers raise the ethical implications of controlling source input A to manipulate target voice B, particularly in cases of defamation and sabotage of target B’s words. This paper analyzes the potential of cycle-consistent GANs in deceptive voice-voice conversion by manipulating interview excerpts of political candidates.

We first review the literature on machine behavior which may contain the interaction between machines, and then discuss the methodology and the related case involving collaboration between robots to instantiate the concept with AI empowerment. The navigation case aims to a security application with autonomous control and roadside agent embedded in a mobile edge computing structure. In the studied navigation based on the artificial potential field method, robots need to use position information to calculate the moving direction frequently. In the case of high motion speed as well as GPS positioning error, the path trajectory may show a sharp change of direction. In order to mitigate the trajectory oscillation, this paper proposes a path planning design where training process and motion direction prediction are integrated by using artificial neural network. The auxiliary navigation agent near multiple obstacles can first extract the past movement information of the robot and then determines whether there is a serious path jitter event. Computer simulation analysis shows that the combination of autonomous control and cooperative behavior can effectively reduce the path jitter so as to achieve a fast and safe path planning.

This paper evaluates the feasibility of applying Massive MIMO to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), devised to exclusively consume data traffic from Machine Type Communications (MTC) devices, the capacity (i:e:, number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time-frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically as well as the MMSE estimator with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines towards a better understanding of the use of massive MIMO in MTC networks.

Bike sharing systems are a key element of a smart city as they have the potential for reducing pollutant emissions and traffic congestion thus substantially improving citizens’ quality of life. In these systems, bicycles are made available for shared use to individuals on a very short-term basis. They are rented in a station and returned in any other station with free docks. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty. The occupancy level of the stations can be constantly monitored through IoT-based services. The goal of this work is to analyze occupancy level data acquired from real systems to discover situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type, called Occupancy Monitoring Pattern (OMPs), is proposed to characterize situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, OMPs can be filtered and evaluated by considering also the spatial and temporal correlation of the acquired measurements. The results achieved on real Smart City data highlight the potential of these techniques in supporting domain experts in maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving the user experience, such as suggesting alternative stations in the neighborhood.

While the mobile phone is the world’s most popular media device, it is actually not one single medium, but is effectively used as a different medium by different user groups. The article characterizes polymodal differences in mobile apps usage among different user groups, including gender, education, occupation, screen size, and price. We monitored the complete app usage of 10,725 smartphone users for one month each (56 million sessions, recording almost 1 million hours). Our key contribution consists in developing and analyzing a theoretical framework to classify the over 16,000 apps used into five categories. Exploring nine research questions we provide a broad characterization by asking who, with which characteristics, uses which kinds of apps in what extensity and intensity? For example, it is not the young and high occupational grades that use the mobile phone as a human-to-machine computer (including gaming and artificial intelligence tools). Large screen size is related to extensive long sessions, while a small screen size is related to intensive frequent usage. The results go beyond providing ample empirical evidence for the inherently polymodal nature of the mobile phone, but also proposes a framework on how to possibility deal with it analytically.

Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4,000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. At this time, our model delivers the highest predictive power on estrogen receptor agonists in the world. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules.

Smart grids represent the evolution of traditional electrical grids through the integration of digital and communication technologies. These grids are capable of monitoring and managing the distribution of electricity efficiently and resiliently. Artificial Intelligence (AI) plays a crucial role in this context, offering advanced solutions for optimization, forecasting, and energy management. This paper explores the main AI technologies applied to smart grids, highlighting their advantages, challenges, and impacts on energy efficiency and sustainability. The methodology employed includes a comprehensive literature review, analysis of current AI applications in smart grids, and examination of case studies to illustrate the practical implementation and benefits of AI technologies in this field.

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we propose the consolidation of virtual machines (VMs) to optimize resource utilization and reduce operational costs. This paper introduces a novel algorithm, leveraging the Modified Artificial Feeding Birds Algorithm (ModAFBA), to manage cost optimally in cloud data centers by reducing power usage while meeting service level agreements (SLAs). The ModAFBA algorithm, inspired by bird foraging behavior, dynamically adjusts solutions based on environmental changes. Comprehensive experimental evaluation demonstrates the superiority of the proposed algorithm in terms of power usage, migration count, and SLA compliance. Through this research, we aim to advance cost management and resource optimization in cloud data centers, providing a valuable tool for cloud service providers to enhance infrastructure efficiency and achieve cost savings.

This study investigates the impact of real-time emotional feedback on the quality of teamwork conducted over videoconferencing. We developed a framework that provides real-time feedback through a virtual mirror based on facial and voice emotion recognition. In an experiment with 28 teams (84 participants), teams collaborated over Zoom to set up a virtual Mars station using custom simulation software. Participants were divided into 14 experimental teams which were shown the virtual mirror, and 14 control teams without it. Team performance was measured by the improvement in the Mars simulation output quality. Our analysis using correlation, multilevel regression, and machine learning revealed that fewer interruptions but an increasing number over time correlated with higher performance. Higher vocal arousal and happiness also enhanced performance. We confirmed that female presence in teams boosts performance. SHAP values indicated that high variability in happiness, head movement, and positive facial valence—an “emotional rollercoaster”—positively predicted team performance. The experimental group outperformed the control group, suggesting that virtual mirroring improves virtual teamwork and that interrupting each other more, while speaking less, leads to better results.

In this study, I will present some documentary evidence that Carlos Drummond de Andrade’s poem “A Máquina do Mundo” [The Machine of the World] (1949) was based on prior reflections contained in a forgotten newspaper article entitled “Poesia e Pesadelo” [Poetry and Nightmare] (1949). In that article, Drummond critically analyzed some of the theses of Stephen Spender's essay “What Is Modern in Modern Poetry” (1948), which had just been published in The Tiger's Eye magazine. As soon as he read Spender's text, Drummond wrote “Poetry and Nightmare” and began to elaborate a narrative poem whose protagonist would be a machine. All the documentary evidence indicates that Drummond took Spender's lessons as touchstones for the elaboration of the narrative and allegorical structure of “The Machine of the World.” According to the interpretation I am proposing here, Drummond, in writing the machine poem, put into practice at least six lessons drawn from Spender’s essay, namely: 1) the modern poet must represent the tension between the external machinelike landscape and the landscape of the inner world of the creative imagination; 2) he must also represent the machinelike, ugly, and urban world around him in a beautiful and poetic way; 3) he must avoid the misconceptions of excessive utopian visionarism, as well as creeping realism; 4) he must reject the imperative of political engagement, whose principle of action states that poetry has a transformative mission in society; 5) he must free himself from the obsession with free verse as the only form of modern poetry and start using the traditional fixed forms; 6) he must understand machines as poetic symbols of human passions, and not just as exotic objects foreign to human reality. My conclusion is that, after reading Spender’s essay, Drummond changed his aesthetic and philosophical understanding of what defines the essence of modern poetry, both in terms of form and content.

YouTube is a very popular social media platform in today’s digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments on food-related videos on YouTube, specifically within two pivotal food product categories: Plant-Based and Hedonic. Our research involved labelling comments using sentiment lexicons such as TextBlob and VADER. Furthermore, the sentiments of the comments were classified using advanced Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and f1-score. Results from VADER showcased a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these tasks are impressive, they fall short in applying common sense reasoning to new situations, filling in information gaps, or understanding and applying unspoken rules or norms. Human intelligence uses both associative memory and event-driven transaction history to make sense of what they are observing fast enough to do something about it while they are still observing it. In addition to this cognitive ability, all bio-logical systems exhibit autopoiesis and self-regulation. In this paper, we demonstrate how machine intelligence can be enhanced to include both associative memory and event-driven transaction history to create a new class of knowledge-based assistants to augment human intelligence. The digital assistants use global knowledge derived from the Large Language Models to bridge the knowledge gap between various participants interacting with each other. We use the general theory of information and schema-based knowledge representation to create the memory and history of various transactions involved in the interactions.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

This paper explores if plants are capable of responding to human movement by changes in their electrical signals. Towards that goal we conducted a series of experiments, where humans over a period of 6 months were performing different types of eurythmic gestures in proximity of garden plants, namely salad, basil, and tomatoes. To measure plant perception, we used the plant spikerbox, a device that measures changes in voltage differentials of plants between roots and leaves. Using machine learning, we find that the voltage differentials over time of the plant predict if (a) eurythmy has been performed, and (b) which kind of eurythmy gestures has been performed. We also find that the signals are different based on the species of the plant. In other words, the perception of a salad, tomato, or basil might differ just as perception of different species of animals differ. This opens new ways of studying plant ecosystems, while also paving the way to use plants as biosensors for analyzing human movement.

Merkel cell carcinoma (MCC) is a rare, highly aggressive skin cancer characterized by two etiologies: the first one, observed in most cases, is attributed to Merkel cell polyomavirus (MCPyV), a DNA tumor virus, while the second one is due to ultraviolet (UV) radiation exposure. Currently, there are limited diagnostic, prognostic, and therapeutic biomarkers available for MCC. MicroRNAs (miRNAs) are small RNA molecules that do not code for proteins but regulate various cellular processes, including cell cycle progression, proliferation, differentiation, and apoptosis. The dysregulation of miRNAs has been associated with a wide range of cancers since they can act as either tumor suppressors or oncogenes. This paper aims to collect, summarize and re-organize the recent discoveries concerning miRNAs, whose dysregulation in the pathogenesis and progression of MCC is slowly emerging. Furthermore, the potential clinical applications of miRNAs as diagnostic biomarkers, possible differentiation between MCPyV-related and unrelated MCC, predictors of response to therapy and prognostics are also discussed. From this review, it emerges that miRNAs may have, in the near future, the potential to become clinically significant in improving the diagnostic accuracy, prognostic evaluation, and therapeutic options of MCCs. Finally, the new perspectives on the poorly explored field of artificial intelligence in MCC are provided.

The storm 'Daniel' has caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. Causal relationships between soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. Results of causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and available phosphorus (P-Olsen), as well as between sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25 and 30 cm). Then linear regression equations were fitted across the different levels of deposit depth to determine the effect of deposit depth on CaCO3, P, and Mn. The results show that there is a linear slope of 0.12, -0.16, and 0.13, for CaCO3, P, and Mn with deposit depth, respectively. Statistical analysis indicates that corn growing in soils with sediment over 10 cm requires a 31.8% increase in P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed.

Using the potent tools of information geometry (IG), the current research analyses the information geometrics of the five-dimensional HTBFC Manifold. By enabling a unique revolutionary analysis for the performance of Human-Trust Based Feedback Control (HTBFC), the integration of information geometry into HTBFC facilitates the relativistic analysis of the five-dimensional HTBFC manifold. Both the two- and three-dimensional manifold subcases are investigated. This work provides a ground-breaking approach to IG unification of Human- Machine Interactions (HMIs). Some potential applications of HTBFC to robotics are presented.Conclusions are provided, along with some emerging open problems and recommendations for future research.

This study introduces a neural network-based approach to predict dust emission, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), the research predicts PM2.5 emissions by analyzing key operational parameters of an advanced almond harvester. The model is trained on extensive field data from the almond pickup system, including variables like brush speed, angular velocity, and harvester forward speed. The results demonstrate a notable predictive accuracy of the FNN model, with a Mean Squared Error (MSE) of 0.02 and a Mean Absolute Error (MAE) of 0.01, indicating a high degree of precision in forecasting PM2.5 levels. The study also finds a strong correlation between certain operational parameters and PM2.5 emissions, highlighting specific areas for optimization in harvesting techniques. By integrating machine learning with agricultural practices, this research provides a significant tool for environmental management in almond production, offering a method to reduce harmful emissions while maintaining operational efficiency. This model presents a solution for the almond industry and sets a precedent for applying predictive analytics in sustainable agriculture.

In the wake of increasing digital fraud, this paper introduces an innovative application of Artificial Intelligence (AI) in detecting fraudulent activities across finance, healthcare, and e-commerce sectors. It presents a detailed analysis of machine learning methodologies, specifically focusing on the advantages of supervised, unsupervised, and deep learning techniques. The paper addresses the challenges such as data imbalance, model interpretability, and ethical implications in AI-based fraud detection. It also discusses the necessity of high-quality datasets and advocates for the integration of traditional and advanced machine learning methods to enhance accuracy and adaptability in fraud identification. However, it acknowledges the limitations including computational demands and overfitting risks. The study underscores the importance of collaborative efforts between AI experts and industry professionals to develop ethical, efficient, and reliable AI solutions for fraud detection.

In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff to patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of sensor data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different sensors by relabeling to the sensors with less samples resolving data imbalance. Standard deviation and Kullback-Leibler divergence between minority and majority class are used to measure signal pattern to find matching sensors to relabel. By matching sensors between classes, two variation of relabeling are implemented specifically full and partial matching. The performance is evaluated using real world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the sensor data with our proposed relabeling method for data augmentation, we achieved higher minority class F1-score as compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected sensor data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

This paper introduces a novel ensemble adjustment Kalman filter (EAKF) that integrates a machine-learning approach. The approach employs nonlinear variable relationships established by a deep neural network (DNN) during the analysis stage of the EAKF. This process nonlinearly projects observation increments into the state variable space. The newly developed DNN-EAKF algorithm can be applied to coupled data assimilation using coupled ocean-atmosphere models. It enhances cross-component updates in strongly coupled data assimilation (SCDA) by diminishing errors in estimating cross-component error covariance arising from insufficient ensemble members, thereby improving the SCDA analysis. This paper employs a conceptual model to conduct twin experiments, validating the DNN-EAKF’s capability to utilize cross-component observation information in SCDA effectively. The approach is anticipated to offer insights for future methodological integrations of machine learning and data assimilation and provide methods for SCDA applications in coupled general circulation models.

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop, assessing seed viability of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible-Near Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500-1030 nm as a non-destructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50°C over 24-hour intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky-Golay (SG) and Multiplicative Scatter Correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of meta-heuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), Genetics (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 seconds) and LCA (44.9389 seconds) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected by the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), Multilayer Perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and NaiveBayes (NB) classifiers. The DSOS-DT and FOA-MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS-LR and DSOS-KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and non-destructive approach for detecting seed viability.

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice, affecting millions of individuals worldwide. The management of AF presents significant challenges due to its complex nature and diverse patient profiles. In recent years, the integration of artificial intelligence (AI) and machine learning technologies has emerged as a promising approach to enhance various aspects of AF management. This comprehensive review aims to explore the evolving role of AI in AF management, with a focus on its potential applications in diagnosis, risk prediction, treatment selection, and patient monitoring. Real academic references were employed to provide evidence-based insights into the impact of AI on AF management.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

More and more people quantify their sleep using wearables and are getting obsessed in their pursuit of optimal sleep (“orthosomnia”). However, it is criticized that many of these wearables are giving inaccurate feedback and even can lead to negative daytime consequences. Acknowledging these facts, we here aim to extend previous findings [] in a new sample of 136 self-reported poor sleepers by implementing optimization procedures to minimize erroneous classification when ambulatory sensing sleep. Firstly, here, we introduce an advanced interbeat-interval (IBI) quality control using a random forest method to account for wearable recordings in naturalistic and more noisy settings. We further aim to improve sleep classification by opting for a loss function model instead of overall epoch-by-epoch accuracy to avoid model biases towards the majority class (i.e., “light sleep”). Using these implementations, we compare the classification performance between the optimized (loss function model) and the accuracy model. We use signals derived from PSG, one-channel ECG, and two consumer wearables: the ECG breast belt Polar® H10 (H10) and the Polar® Verity Sense (VS), an optical Photoplethysmography (PPG) heart-rate sensor. Results reveal high overall accuracy for the new model for ECG (86.3 %, κ=.79), H10 (84.4%, κ=.76), and VS (84.2%, κ=.75) with intended improvements in deep sleep and wake. In addition, the new optimized model displays moderate to high correlations and agreement with PSG on primary sleep parameters, while measures of reliability, expressed in intra-class correlations, suggest excellent reliability for most sleep parameters. Finally, it is demonstrated that the new model is still classifying sleep accurately in 4-classes in users taking heart-affecting and/or psychoactive medication which can be considered a prerequisite for use also in high age groups and/or with common disorders. Further improving and validating sleep stage classification algorithms with affordable wearables may resolve existing scepticism and open the door for such approaches in clinical practice.

The large quantity of information retrieved from communities, public data repositories, web pages, or data mining can be sparsed and poorly classified. This work shows how to employ unsupervised classification algorithms such as K-means proper to classify user reviews into their closest category, forming a balanced data set. Moreover, we found that the text vectorization technique significantly impacts the clustering formation, comparing TF-IDF and Word2Vec. The value for mapping a cluster with movie genre was 81.34% ± 20.48 of the cases when the TF-IDF was applied, whereas Word2Vec only yielded a 53.51% ± 24.1. In addition, we highlight the impact of the removal of stop-words. Thus, we detected that pre-compiled lists are not the best method to remove stop-words before clustering because there is much ambiguity, centroids are poorly separated, and only 57% of clusters could match a movie genre. Thus, our proposed approach achieved a 94% of accuracy. After analyzing the classifiers’ results, we appreciated a similar effect when divided by the stop-words method removal. Statistically significant changes were observed, especially in precision metric and Jaccard scores in both classifiers, using custom-generated stop lists rather than pre-compiled ones. Reclassifying sparse data is strongly recommended as using custom-generated stop lists.

Sensor fusion algorithms and models have been widely used in recent times. Although research evidence has informed the use of sensor fusion models in diverse applications, there is room for improvement, especially in home-based health monitoring applications which require less supervision and technical knowledge of users. The present work compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. 574 privacy-friendly (binary) images and 1,722 datasets gleaned from thermal and Radar sensing solutions respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, K-Nearest Neighbours and CN2 induction. Further evaluation of the sensor data fusion framework based on cross validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% Precision and 96.4% for Recall.

Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species, electromagnetic emissions generated through gas plasma jets, have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets of machine learning, such as reinforcement learning, and deep learning. This review emphasizes the significant application of AI algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of AI in plasma medicine, requires research efforts, data sharing and interdisciplinary collaborations. Unravelling the complex mechanisms, developing real-time diagnostics, and optimizing AI models will be crucial to harness the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.

The rapid progress in artificial intelligence, machine learning, and natural language processing has led to the emergence of increasingly sophisticated large language models (LLMs) enabling their use in healthcare. The study assesses the performance of two LLMs: the GPT-3.5 and GPT-4 models in passing the medical examination for access to medical specialist training in Spain MIR. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. We studied the 2022 Spanish MIR examination after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM ChatGPT4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length and question sequence d performance. GPT-4 outperformed GPT -3.5, scoring 86.81% in Spanish (p<0.001). English translations had a slightly enhanced performance. Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, with specialties like Pharmacology, ICU, and Infectious Diseases showing lower performance. The error analysis revealed that while a 13.2% error rate existed, gravest categories like "error requiring intervention to sustain life" and "error resulting in death" had a 0% rate. Conclusions: GPT-4 performs robustly on the Spanish MIR examination, varying its capability to discriminate knoweldge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implication on patient safety.

A land cover map of two arctic catchments, nearby the Abisko Scientific Research Station, was obtained from a classification of a Sentinel-2 satellite image and a ground survey performed in July 2022. The two contiguous catchments, Miellajokka and Stordalen, are covered by various ecotypes, from boreal forest to alpine tundra and peatland. Two classification algorithms, support vector machine and random forest, were tested and gave very similar results. The percentage of correctly classified pixels was over 88% in both cases. The developed workflow relies solely on open source software and acquired ground observations. Space organization was directed by the altitude as demonstrated by the intersection of the land cover with the topography. Comparison between this new land cover map and previous ones based on data acquired between 2008 and 2011 shows some trends of vegetation cover evolution in response to climate change in the considered area. This land cover map is key input data for permafrost modeling, and hence for the quantification of climate change impacts in the studied area.

Intracortical microelectrode arrays (MEAs) can be used in a range of applications, from basic neuroscience research to providing an intimate interface with the brain as part of a Brain-Computer Interface (BCI) system aimed to restore function for people living with neurological disorders or injuries. Unfortunately, MEAs tend to fail prematurely, leading to a loss in functionality for many applications. An important contributing factor in MEA failure is oxidative stress resulting from chronically inflammatory-activated microglia and macrophages releasing reactive oxygen species (ROS) around the implant site. Antioxidants offer a means for mitigating oxidative stress and improving tissue health and MEA performance. Here, we investigate using the clinically available antioxidant dimethyl fumarate (DMF) to reduce the neuroinflammatory response and improve MEA performance in a rat MEA model. Daily treatment of DMF for 16 weeks resulted in a significant improvement in the recording capabilities of MEA devices during the sub-chronic (Weeks 5-11) phase (42% active electrode yield vs. 35% for control). However, these sub-chronic improvements were lost in the chronic implantation phase, as a more exacerbated neuroinflammatory response occurs in DMF-treated animals by 16 weeks post-implantation. Yet, neuroinflammation was indiscriminate between treatment and control groups during the sub-chronic phase. Although worse for chronic use, a temporary improvement (&lt;12 weeks) in MEA performance is meaningful. Providing short-term improvement to MEA devices using DMF can allow for improved use for limited-duration studies. Further efforts should be taken to explore the mechanism behind a worsened neuroinflammatory response at the 16-week time point for DMF-treated animals to assess its usefulness to specific applications.

PM-Gati-Shakti Initiative, integration of ministries, including railways, ports, waterways, logistic infrastructure, mass transport, airports, and roads. Aimed at enhancing connectivity and bolstering the competitiveness of Indian businesses, the initiative focuses on six pivotal pillars known as "Connectivity for Productivity": comprehensiveness, prioritization, optimization, synchronization, analytical, and dynamic. In this study, we explore the application of these pillars to address the problem of "Maximum Demand Forecasting in Delhi." Electricity forecasting plays a very significant role in the power grid as it is required to maintain a balance between supply and load demand at all times, to provide a quality electricity supply, for Financial planning, generation reserve, and many more. Forecasting helps not only in Production Planning but also in Scheduling like Import / Export which is very often in India and mostly required by the rural areas and North Eastern Regions of India. As Electrical Forecasting includes many factors which cannot be detected by the models out there, We use Classical Forecasting Techniques to extract the seasonal patterns from the daily data of Maximum Demand for the Union Territory Delhi. This research contributes to the power supply industry by helping to reduce the occurrence of disasters such as blackouts, power cuts, and increased tariffs imposed by regulatory commissions. The forecasting techniques can also help in reducing OD and UD of Power for different regions. We use the Data provided by a department from the Ministry of Power and use different forecast models including Seasonal forecasts for daily data.

Accurate prediction of crop production is essential in effectively managing agricultural countries' food security and economic resilience. This study evaluates the performance of statistical and machine learning-based methods for large-scale crop production forecasting. We predict the quarterly production of 325 crops (including fruits, vegetables, cereals, non-food, and industrial crops) across 83 provinces in the Philippines. Using a comprehensive dataset of 10,949 time series over 13 years, we demonstrate that a global forecasting approach using a state-of-the-art deep learning architecture, the transformer, significantly outperforms traditional local forecasting approaches built on statistical and baseline methods. By leveraging cross-series information, our proposed way is scalable and works well even with time series that are short, sparse, intermittent, or exhibit structural breaks/regime shifts. The results of this study further advance the field of applied forecasting in agricultural production and provide a practical and effective decision-support tool for policymakers that oversee the farm sector on a national scale.

In the past decades, the world is actively working towards the global goal of net-zero emission. To decrease emissions, there is a notable trend of electrification in transportation, which is a transition from traditional fuel-based systems to electrical power systems onboard different transportation platforms. For this transition, it is important to study the electrical structure, specifically the onboard microgrid, powered by various energy sources. In this paper, traditional energy management strategies for onboard microgrid systems are discussed, which usually require complicated optimization algorithms and high computation capabilities. Driven by the recent advancements in information technologies, artificial intelligence and digital twin have gained much interest within the transportation sector. These technologies can effectively utilize data to achieve intelligent decision-making, optimize resource utilization, and save energy consumption. This paper presents an overview of the usage of these emerging information technologies in energy management strategies, providing an overall summary and classification of the practical applications. In addition, after examining the potential challenges associated with artificial intelligence and digital twin, this paper also discusses future trends in this field.

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing, data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai'i island, using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that, while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m x 2 m M. polymorpha presence dataset and a 30 m x 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

In this study, our primary objective was to analyze the tradeoff between accuracy and complexity in machine learning models, with a specific focus on the impact of reducing complexity and entropy on the production of landslide susceptibility maps. We aimed to investigate how simplifying the model and reducing entropy can affect the capture of complex patterns in the susceptibility maps. To achieve this, we conducted a comprehensive evaluation of various machine-learning algorithms for classification tasks. We compared the performance of these algorithms in terms of accuracy and complexity, considering both "before" and "after" scenarios of dimensionality reduction using Principal Component Analysis (PCA). Our findings revealed that reducing complexity and lowering entropy can lead to an increase in model accuracy. However, we also observed that this reduction in complexity comes at the cost of losing important complex patterns in the produced landslide susceptibility maps. By simplifying the model and reducing entropy, certain intricate relationships and uncertain patterns may be overlooked, resulting in a loss of information and potentially compromising the accuracy of the susceptibility maps. The analysis encompassed a diverse range of machine learning algorithms, including Random Forest (RF), Extra Trees (EXT), XGboost, LightGBM, Catboost, Naive Bayes (NB), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), and Decision Trees (DT). Each algorithm was evaluated for its strengths and limitations, considering the tradeoff between accuracy and complexity. Before dimensionality reduction, the algorithms demonstrated promising results, with RF exhibiting excellent AUC/ROC scores and average accuracy. However, computational costs were noted as a potential drawback for RF, especially when dealing with large datasets. EXT showcased robust performance and good accuracy, while XGboost demonstrated its ability to handle complex relationships within large datasets, albeit requiring careful hyperparameter tuning. The efficiency and scalability of LightGBM made it a suitable choice for large datasets, although it displayed sensitivity to class imbalance. Catboost excelled in handling categorical features, but longer training times were observed for larger datasets. NB showcased simplicity and computational efficiency but assumed independence among features. KNN, known for its capability to capture local patterns and spatial relationships, was found to be sensitive to the choice of distance metric. GBM, while capturing complex relationships effectively, was prone to overfitting without proper regularization. DT, with its interpretability and ease of understanding, faced limitations in terms of overfitting and limited generalization. After dimensionality reduction, certain algorithms exhibited improvements in their AUC/ROC scores and average accuracy, including RF, EXT, XGboost, and LightGBM. However, for a few algorithms, such as NB and DT, a decrease in performance was observed. This study provides valuable insights into the performance characteristics, strengths, and limitations of various machine learning algorithms in classification tasks. Researchers and practitioners can utilize these findings to make informed decisions when selecting algorithms for their specific datasets and requirements. We also aim to identify the potential factors contributing to the high accuracy rates obtained from these ensembled algorithms and explore possible shortcomings of non-ensembled algorithms that may result in lower accuracy rates. By conducting a comprehensive analysis of these algorithms, we seek to provide valuable insights into the benefits and limitations of ensembled approaches for landslide susceptibility mapping. Our study sheds light on the challenges faced when balancing accuracy and complexity in machine learning models for landslide susceptibility mapping. It emphasizes the importance of carefully considering the level of complexity and entropy reduction in relation to the specific patterns and uncertainties present in the data. By providing insights into this tradeoff, our research aims to assist researchers and practitioners in making informed decisions regarding model complexity and entropy reduction, ultimately improving the quality and interpretability of landslide susceptibility maps.

The paper proposes a rock classification method using the vibration signal of the rotary drilling process. This proposed method classifies four rocks with a reliability of 100 %, from the vibration signal record of a lasting 1/4 second. For the design of a suitable classification method, several attributes of the vibration signal were calculated for two different signal recording lengths. Cluster dendrogram, ANOVA test, and boxplot were used to determine useful attributes and proper signal length. The classification rule was found using a decision tree, machine learning tool. The publication gradually describes the process of creating the classification method and the results of the reliability verification of the proposed classification method. Rock disintegration by rotary drilling was carried out under standardized experimental conditions. The disintegration rocks were andesite, granite, limestone, and concrete.

The increasing prevalence of depression worldwide requires more effectiveness in therapy approaches and a molecular understanding of antidepressants mode of action. We carried out untargeted metabolomics of the prefrontal cortex of rats exposed to chronic social isolation (CSIS), a rat model of depression, and/or fluoxetine treatment using liquid chromatography-high resolution mass spectrometry. The behavioral phenotype was assessed by the forced swim test. To analyze metabolomics data, univariate and multivariate analysis and biomarker capacity assessment using the classical receiver operating characteristic (ROC) curve were used. Support vector machine-linear kernel (SVM-LK), as a machine-learning algorithm was performed for binary classification. Upregulated myo-inositol following CSIS may represent a potential marker of depressive phenotype. Effective fluoxetine treatment reversed depressive-like behavior and increased sedoheptulose 7-phosphate, hypotaurine, and acetyl-L-carnitine contents, which were identified as potential markers. We identified 4 or 10 marker candidates with ROC curve greater than 0.9 for CSIS or fluoxetine effectiveness designation. SVM-LK has given accuracy of 61.50%, or 93.30%, and 7 or 25 predictive metabolites for CSIS vs. control and fluoxetine-treated CSIS vs. CSIS classification. Overall, metabolic fingerprints combined with ROC curve and SVM-LK may represent a new approach to identifying potential markers or predicting metabolites for group designation or classification.

The deep energy method (DEM), a type of physics-informed neural network, is evolving as an alternative to finite element analysis. This method employs the principle of minimum potential energy to predict deformations under static loading conditions. However, the model’s accuracy is contingent upon choosing the appropriate architecture for the model, which can be challenging due to the high interactions between hyperparameters, large search space, difficulty in identifying objective functions, and non-convex relationships with the objective functions. To improve DEM’s accuracy, we first introduce random Fourier feature (RFF) mapping. RFF mapping helps with the training of the model by reducing bias towards high frequencies. The effects of six hyperparameters are then studied under compression, tension, and bending loads in planar linear elasticity. Based on this study, a systematic automated hyperparameter optimization approach is proposed. Due to the high interaction between hyperparameters and the non-convex nature of the optimization problem, Bayesian optimization algorithms are used. The models trained using optimized hyperparameters and having Fourier feature mapping can accurately predict deflections compared to finite element analysis. Additionally, the deflections obtained for tension and compression load cases are more sensitive to variations in hyperparameters than bending.

Election outcomes have been predicted in the past with the help of various state-of-the-art language models. Sentiment analysis helps in establishing the opinions of the public about a particular subject, a popular experiment known as opinion mining. Twitter has grown in popularity and proven to be a key tool in mining people’s sentiments concerning election and other trending subjects of interest. The outcome of the just concluded Presidential election in Nigeria shifts the focus on Lagos State governorship election. In this study, we propose a Bidirectional Encoder Representations from Transformers (BERT) model for the sentiment analysis of governorship election in Lagos State Nigeria using Twitter data. A total of 800,000 personal and public tweets were scraped from twitter concerning the three prominent contesting candidates using carefully selected search queries. The tweets were preprocessed to avoid noise and inconsistencies. The preprocessed tweets were passed into the pretrained and finetuned BERT model. The result was analyzed to establish the sentiments of the public about the candidates. The social networks of the candidates were also analyzed. The parameter-tuning yield different results with different learning rates (LR). Results showed that the learning rate at 1e-7 gave the best performance and that the smaller the learning rate, the higher the accuracy but the larger the epoch size, the higher the accuracy.

Physiologically Based Pharmacokinetic Models (PBPK) are mechanistical tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognised by regulatory authorities for predicting organ concentration-time profile, pharmacokinetic and daily intake dose of xenobiotics. Extension of PBPK models to capture sensitive populations like pediatric, geriatric, pregnant females, fetus etc. and diseased population like renal impairment, liver cirrhosis etc. is a must. However, the current modelling practice and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology, and calculation of biochemical parameters for integrating the knowledge and refining existing PBPK models. Specific PBPK covering compartments like cerebrospinal fluid, and hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints like developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in-silico models where experimental data is unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building qAOPs, use of machine learning for improving existing models along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.

Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as a tool for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Past studies have established people’s opinion elections using social media posts. The advent of state-of-the-art algorithms for unstructured text processing implies tremendous progress in natural language processing and understanding. Aim: In this work, a Natural Language framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Raw datasets concerning discourse around Nigeria 2023 elections from Twitter of 2,059,113 18 dimensions were collected. Sentiment analysis was performed on the preprocessed dataset using three different machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. Personal tweet analysis of the three candidates provided insight on their campaign strategies and personalities while public tweet analysis established the public’s opinion about them. The performance of the models was also compared using accuracy, recall, false positive rate, precision and F-measure. Results: LSTM model gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2% , 87.6% and 82.9% respectively; the BERT model gave an accuracy, precision, recall, AUC and f-measure of 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively while the LSVC model gave an accuracy, precision, recall, AUC and f-measure of 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively. Conclusion: The experimental results show that sentiment analysis and other Natural Language Processing tasks can aid in the understanding of the social media space. Results also revealed the leverage of each aspirant towards winning the election. We conclude that sentiment analysis can form a general basis for generating insights for election and modeling election outcomes.

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we therefore explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985-2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5-8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash-Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolut Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a Combined forecast based on the best performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022-2029. In the majority of the catchments the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

Sense element engagement theory explains how neural networks produce cortical prosthetic vision. A major prediction of the theory can be tested by developing a device which is expected to enable perception of continuous forms in altered visual geometries. The research reported here completes several essential steps in developing this device: (1) replication of simulations that are consistent with the theory using the NEST simulator, which can also be used for full-scale network emulation by a neuromorphic computer; (2) testing whether results consistent with the theory survive increasing the scale and duration of simulations; (3) establishing a method that uses numbers of spikes produced by network neurons to report the number of phosphenes produced by cortical stimulation; and (4) simulating essential functions of the prosthetic device. NEST simulations replicated early results and increasing their scale and duration produced results consistent with the theory. A decision function created using multinomial logistic regression correctly classified the expected number of phosphenes for 2080 spike number distributions for each of three sets of data, half of which arise from simulations expected to yield continuous visual forms on an altered visual geometry. A process for modulating electrical stimulation amplitude based on intermittent population recordings that is predicted to produce continuous visual forms was successfully simulated. The classification function developed using logistic regression will be used to tune this process as the scale of simulations is further increased.

Lumbar support exoskeletons with active and passive actuators are currently the cutting-edge technology for preventing back injuries in workers while lifting heavy objects. However, many challenges still exist in both types of exoskeletons, including rigid actuators, risks of human-robot interaction, high battery consumption, bulky design, and limited assistance. In this paper, the design of a compact, lightweight energy storage device combined with rotary series elastic (ES-RSEA) is proposed for use in a lumbar support exoskeleton to increase the level of assistance and exploit the human bioenergy during the two stages of the lifting task. ES takes the responsibility to store and release passive mechanical energy while RSEA provides excellent compliance and prevents injury from the human body's undesired movement. The experimental tests on the spiral spring showed excellent linear characteristics (above 99%) with an actual spring stiffness of 9.96 Nm/rad. The results demonstrate that ES-RSEA can provide maximum torque assistance in the ascent phase with 66.6 Nm while generating nearly 21 Nm of spring torque during descent without turning on the DC motor. Ultimately, the proposed design can maximize the energy storage of human energy, exploit the biomechanics of lifting tasks, and reduce the burden on human effort to perform lifting tasks.

Japan Aerospace Exploration Agency (JAXA) is going to launch Advanced Land Observing Satellite 3 (ALOS-3) after 2022. ALOS-3 satellite is capable of observing global land areas with wide swath (4000 km along-track direction and 70 km cross-track direction) at high spatial resolution (panchromatic: 0.8m, multispectral: 3.2m). Maintenance and updating of land cover and vegetation information at national level is one of the major goals of the ALOS-3 mission. This paper presents the potential of simulated ALOS-3 images for the classification and mapping of land cover and vegetation types at Genus-Physiognomy-Ecosystem (GPE) level. We acquired and simulated WorldView-3 images according to the configuration of the ALOS-3 satellite sensor and the simulated ALOS-3 images were utilized for the classification and mapping of land cover and vegetation types in three sites (Hakkoda, Zao, and Shiranuka) in northern Japan. This research dealt with 17 land cover and vegetation types in Hakkoda site, 25 land cover and vegetation types in Zao site, and 12 land cover and vegetation types in Shiranuka site. Ground truth data were newly collected in three sites, and we employed eXtreme Gradient Boosting (XGBoost) classifier with the implementation of 10-fold cross-validation method for assessing the potential of ALOS-3S images. The classification accuracies obtained in Hakkoda, Zao, and Shiranuka sites in terms of f1-score were 0.810, 0.729, and 0.805 respectively. The fine scale (3.2m) land cover and vegetation maps produced in the study sites showed clear and detailed view of the distribution of plant communities. Regardless of the limited number of the temporal images, ALOS-3S images showed high potential (at least 0.729 F1-score) for the land cover and vegetation classification in all three sites. The availability of more cloud free temporal scenes is expected for improved classification and mapping in the future.

This article discusses the use of a hand held electronic nose to obtain information on the presence of some aromatic defects in natural cork stoppers, such as haloanisoles, alkylmethoxypyrazines and ketones. The proposed prototype has been developed as an instrumentation system with up to eight commercial gas sensors. Machine learning algorithms such as k-nearest neighbors and artificial neural networks has been used. The use of this system tries to improve the current aromatic defect detection process in the cork stopper industry, which is done by gas chromatography or human test panels.

To achieve the near zero emission of wastewater in the flue gas desulfurization (FGD) system in coal-fired power plant and better utilize the exhaust heat from flue gas, a feasible technology of spraying FGD wastewater in the flue duct for evaporation is discussed in the present study. A full-scale influencing factor investigation on the wastewater droplet evaporation performance is established under the Eulerian-Lagrangian model numerically. The dominant factors, including the characters of wastewater droplets, flue gas and the spray nozzles were analyzed under different conditions, respectively. Considering the multiple factors and conditions in the process, a Least-Square support vector machine (LSSVM) model is introduced to predict the evaporation rate based on the numerical results. Conclusions are made that the flue gas temperature and droplet diameter are of great importance in the evaporation process. The spray direction of droplet parallel with the flue gas flow direction is profitable for the dispersion of droplet, resulting the maximal evaporation rate. A double-nozzle arrangement optimized with relatively small flow rate is recommended. The LSSVM model can accurately predict the evaporation rate using the numerical results with different conditions.

Fault diagnosis of the gearbox is a decisive part of the modern industry to find the many gearbox defects like gear tooth crack, chipped or broken, etc. But sometimes, the nonstationary properties of vibration signal and low energy of minimal faults make this procedure very challenging. Previously, many types of techniques have been developed for gearbox condition monitoring. But most of the methods are dealing with conventional techniques of the gearbox condition monitoring, such as time-domain analysis or frequency domain analysis. Most of the conventional methods are not suitable for the nonstationary vibration signal. Thus, this paper presents a novel gearbox fault diagnosis technique using conditional temporal moments and an optimizable support vector machine (SVM). This work also presents an integrated features extraction technique based on the standard features, i.e., statistical and spectral features with the combinations of moment features. The impact of the four conditional temporal moments of each gearbox condition is also presented. This work shows that the proposed method successfully classifies and categorizes the gearbox faults at an early stage.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning, deep learning and neural networks. AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of ‘big data’ and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-5 of diseases studied using AI; the United States, China, United Kingdom, South Korea and Canada are publishing the most articles in AI research; MIT is the world’s leading university in AI research; and convolutional neural networks are by far the most popular deep learning algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.

The study takes into account different classes of tunnel boring machines (TBMs), with the aim of identifying correlation models which are meant to estimate, at a preliminary design phase, the construction time of a tunnel and to evaluate the mechanical and operational parameters of the TBMs, starting from the knowledge of the tunnel length and/or the excavation diameter. To achieve this goal, first of all a database was created, thanks to the collection of the most meaningful technical parameters from a large number of tunnels; afterward, it was statistically analysed through Microsoft Excel. In a first phase, forecasting models were identified for the three types of machines investigated, separately for compact rocks (open TBM) and fractured rocks (single and double shield TBM). Then, the mechanical parameters collected through the database were analysed, with the aim of obtaining models that take into account, in addition to the type of TBM, the geological aspect, and the type of rock characterising the rock mass. Finally, the validation of the study was proposed in a real case, represented by the Moncenisio base tunnel, a work included in the new Turin–Lyon connection line. The estimated values were compared with the real ones, in order to verify the accuracy of the experimental models identified.

This work presents an alternative to harnessing wind energy with an Electromagnetic Frequency Regulator (EFR) coupled to a hydrostatic transmission and associated with a horizontal axis wind turbine, a bidirectional frequency inverter and a secondary energy source, in a hybridized system. The hydrostatic transmision is composed by a fixed displacement axial piston pump and a variable displacement, swash plate, axial piston motor. Feedback linearization was used as a technique to control the motor geometric displacement, and a prediction algorithm for the steady state rotations of the armature and the electromagnetic field has been developed. A 5\,kW project was simulated on a Scilab platform, with combinations of constant or variable load, and constant or variable wind speed. The results indicated that the system was able to supply the generator load, adapting to fluctuations in wind speed. The possibility of storing wind energy through the inverter has also been proven. The system can accumulate energy in batteries during the fastest wind regimes, to use it when the turbine power is lower than the load.

Purpose The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals has created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of an image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology, etc. Design/methodology/approach The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training Deep Learning (DL) models on thousands of images of these tests using transfer learning, this paper i) classifies the type of the assay, and ii) classifies the colourimetric results. Findings This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time. Originality/value To the best of our knowledge, this is the first attempt to provide Colourimetric Assay Type Classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

During the period January 2014 – October 2018, four strong earthquakes occurred in the Ionian Sea, Greece. A rich aftershock sequence followed each event of them. More analytically, according to the manual solutions of National Observatory of Athens, the first event (K1), occurred on 26 January 2014 in Kefallinia Island with magnitude M_L = 5.8, which was followed by another in the same region (K2) on 3 February 2014 with magnitude M_L = 5.7. The third event occurred on 17 November 2015, M_L = 6.0 in Lefkas Island (L1) and the last on 25 October 2018, M_L = 6.6 in Zande Island (Z1). The first three of these earthquakes caused moderate structural damages mainly in houses and produced particular unrest to the local population. This work presents first the calculation of the source parameters of the strong events as well as for all earthquakes with magnitude M_L > 4.0, using the methodology of the Moment tensor inversion and secondary data from permanent GPS stations were analyzed to confirm the findings from seismological data and to investigate the displacement due to the earthquakes.

In this article, we analyse the computer aid screening method of COVID19 using Xray and CT scan images. The main objective is to set an analytical closure about the computer aid screening of COVID19 among the X-ray image and CT scan image. The computer aid screening method includes deep feature extraction, transfer learning and traditional machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN model. The machine learning approach includes three sets of features and three classifiers. The pre-trained CNN models are alexnet, googlenet, vgg16, vgg19, densenet201, resnet18, resnet50, resnet101, inceptionv3, inceptionresnetv2, xception, mobilenetv2 and shufflenet. The features and classifiers in machine learning approaches are GLCM, LBP, HOG and KNN, SVM, Naive bay’s respectively. In addition, we also analyse the different paradigms of classifiers. In total, the comparative analysis is carried out in 65 classification models, i.e. 13 in deep feature extraction, 13 in transfer learning and 39 in machine learning approaches. Finally, all the classification models perform better in X-ray image set compare to CT scan image set.

A tunable quantum cascade laser (QCL) spectrometer was used to develop methods for detecting and quantifying high explosives (HE) in soil based on multivariate analysis (MVA) and artificial intelligence (AI). For quantification, mixes of 2,4-dinitrotoluene (2,4-DNT) with concentrations from 0% to 20% w/w were investigated using three types of soils: bentonite, synthetic soil, and natural soil. A Partial least squares regression model was generated for predicting 2,4-DNT concentrations. To increase its selectivity, the model was trained and evaluated using additional analytes as interferences, including other HEs such as PETN, RDX, and TNT and non-explosives such as benzoic acid and ibuprofen. For detection, mixes of different explosives in soils were used to implement two AI strategies. In the first strategy, the spectra of the samples were compared with those of soils recorded in a database to identify the most similar soils based on QCL spectroscopy. Next, a Classical Least Squares preprocessing (Pre-CLS) was applied to soils spectra selected from the database. The parameter obtained based on the sum of the weights of Pre-CLS was then used to generate a simple binary discrimination model for distinguishing between contaminated and uncontaminated soils, achieving an accuracy of 0.877. In the second AI strategy, the same parameter was added to a principal component matrix obtained from spectral data of samples and used to generate multi-classification models based on different machine learning algorithms. A Random Forest model worked best with 0.997 accuracy and allowing to distinguish between soils contaminated with DNT, TNT, or RDX and uncontaminated soils.

Prediction of crop yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study, the performance of artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction is evaluated. According to the results, ANNGWO, with R of 0.48, RMSE of 3.19, and MEA of 26.65, proved a better performance in the crop yield prediction compared to the ANN-ICA model. The results can be used by either practitioners, researchers or policymakers for food security.

NMR spectral datasets, especially in systems with limited samples, can be difficult to interpret if they contain multiple chemical components (phases, polymorphs, molecules, crystals, glasses, etc…) and the possibility of overlapping resonances. In this paper, we benchmark several blind source separation techniques for analysis of NMR spectral datasets containing negative intensity. For benchmarking purposes, we generated a large synthetic datasbase of quadrupolar solid-state NMR-like spectra that model spin-lattice T1 relaxation or nutation tip/flip angle experiments. Our benchmarking approach focused exclusively on the ability of blind source separation techniques to reproduce the spectra of the underlying pure components. In general, we find that FastICA (Fast Independent Component Analysis), SIMPLISMA (SIMPLe-to-use-Interactive Self-modeling Mixture Analysis), and NNMF (Non-Negative Matrix Factorization) are top-performing techniques. We demonstrate that dataset normalization approaches prior to blind source separation do not considerably improve outcomes. Within the range of noise levels studied, we did not find drastic changes to the ranking of techniques. The accuracy of FastICA and SIMPLISMA degrades quickly if excess (unreal) pure components are predicted. Our results indicate poor performance of SVD (Singular Value Decomposition) methods, and we propose alternative techniques for matrix initialization. The benchmarked techniques are also applied to real solid state NMR datasets. In general, the recommendations from the synthetic datasets agree with the recommendations and results from the real data analysis. The discussion provides some additional recommendations for spectroscopists applying blind source separation to NMR datasets, and for future benchmark studies. Applications of blind source separation to NMR datasets containing negative intensity may be especially useful for understanding complex and disordered systems with limited samples and mixtures of chemical components.

Machine Learning (ML) approaches have been fruitfully applied to several classification problems of neurophysiological activity. Considering the relevance of emotion in human cognition and behaviour, ML found an important application field in emotion identification based on neurophysiological activity. Nonetheless, the literature results present a high variability depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight on ML applied to emotion identification based on electrophysiological brain activity. For this reason, we recorded EEG activity while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the classifiers performance with static and dynamic (time evolving) features. The results show a clear increased in classification accuracy with temporal dynamic features. In particular, the SVM classifiers with temporal features showed the best accuracy (63.8 %) in classifying high vs. low arousal auditory stimuli.

A large amount of data is produced every day by stakeholders of the Air Traffic Management (ATM) system, in particular airline operators, airports, and air navigation service provider (ANSP). Most data is kept private for many reasons, including commercial and security concerns. More than data, shared information is precious, as it leverages intelligent decision-making support tools designed to smooth daily operations. We present a framework to detect, identify and characterise anomalies in past aircraft trajectory data. It is based on an open source of ADS-B based aircraft trajectories, and extracted information can benefit a wide range of stakeholders: Air Traffic Control (ATC) training centres could play more realistic simulations; ANSP may improve capacity indicators; academics improve safety models and risk estimations; and commercial stakeholders, like airlines and airports, may use such information to improve short-term predictions and optimise their operations. The technique is based on autoencoding artificial neural networks applied on flows of trajectories, which provide a useful reading grid associating cluster analysis with quantified level of abnormality. In particular, we find that the highest anomaly scores correspond to poor weather conditions, whereas anomalies with a lower score relate to ATC tactical actions.

Deep behavioral covariates (DBCs) introduced in this perspective form a new class of covariates that have the potential to enhance the performance of predictive models and improve analytics in clinical decision support applications. DBCs can measure how engaged a patient tends to be and how he or she tends to respond to events, and they may be highly predictive of the patient’s outcomes for a planned treatment. DBCs may potentially serve as a standard to measure patient engagement and activation and may form highly efficient mechanisms for improving patient outcomes.

Dayaoshan, as an important metal ore producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing method is used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference bas been removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information has been extracted based on principal component analysis (PCA) and support vector machine (SVM) method; Thirdly, the favorable areas of gold mining in Gulong has been delineated by using ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; Lastly, field survey verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.

We analyze damage propagation modeling of turbo-engines in a data-driven approach. We investigate subspace tracking assuming a low dimensional manifold structure and a static behavior during the healthy state of the machines. Our damage propagation model is based on the deviation of the data from the static behavior and uses the notion of health index as a measure of the condition. Hence, we incorporate condition-based maintenance and estimate the remaining useful life based on the current and previous health indexes. This paper proposes an algorithm that adapts well to the dynamics of the data and underlying system, and reduces the computational complexity by utilizing the low dimensional manifold structure of the data. A significant performance improvement is demonstrated over existing methods by using the proposed algorithm on CMAPSS Turbo-engine datasets.